To preserve the malicious functionality, researchers generate the evasion samples within semantic limits [1]}, [2]}, [3]}, [4]}, [5]} by attacking the trained classifiers using a DRL agent. The evaded samples were collected to be used for adversarial training to make the model adept at adversarial awareness. Similarly, in [2]}, authors used DRL to attack PDF (Portable Document Format) malware detectors to generate evasive malware samples keeping the functionality of the attacks preserved. The rationale for using the DRL is that we can bound it to explore the evasion attacks within a specific range defined by \(\epsilon \)  in Equation REF . In this paper, we have used the DRL and the EVAGAN model to continuously generate the evasion attack samples using a DRL agent. The process becomes part of the EVAGAN training. The marriage of GAN and DRL culminates RELEVAGAN, which uses the power of EVAGAN as a robust evasion-aware detection model and DRL as the semantics check on the samples generated by the \(\mathcal {G}\)  of EVAGAN. The details of the model will be further discussed in Section .
<FIGURE>